A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification

Too, Jing Wei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Mohd Ali, Nursabillilah and Tee, Wei Hown (2018) A New Competitive Binary Grey Wolf Optimizer To Solve The Feature Selection Problem In EMG Signals Classification. Computers, 7 (4). pp. 1-18. ISSN 2073-431X

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Abstract

Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhancement and feature reduction. Therefore, this article proposes a new competitive binary grey wolf optimizer (CBGWO) to solve the feature selection problem in EMG signals classification. Initially, short-time Fourier transform (STFT) transforms the EMG signal into time-frequency representation. Ten time-frequency features are extracted from the STFT coefficient. Then, the proposed method is used to evaluate the optimal feature subset from the original feature set. To evaluate the effectiveness of proposed method, CBGWO is compared with binary grey wolf optimization (BGWO1 and BGWO2), binary particle swarm optimization (BPSO), and genetic algorithm (GA). The experimental results show the superiority of CBGWO not only in classification performance, but also feature reduction. In addition, CBGWO has a very low computational cost, which is more suitable for real world application.

Item Type:	Article
Uncontrolled Keywords:	Binary grey wolf optimization; Classification; Electromyography; Feature selection; Grey wolf optimizer; Time-frequency feature
Subjects:	T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Electrical Engineering
Depositing User:	Mohd Hannif Jamaludin
Date Deposited:	08 Aug 2019 03:11
Last Modified:	09 Aug 2021 18:25
URI:	http://eprints.utem.edu.my/id/eprint/23003
Statistic Details:	View Download Statistic

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